{"nbformat":4,"nbformat_minor":0,"metadata":{"accelerator":"GPU","colab":{"name":"cnn.ipynb","provenance":[],"collapsed_sections":[],"machine_shape":"hm","authorship_tag":"ABX9TyOsgyk98xvDpRudxnHW+dk4"},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GqDI7gV-dMfF","executionInfo":{"elapsed":4530,"status":"ok","timestamp":1622743710753,"user":{"displayName":"Axel Mukwena","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjkBUMeSLjKE8qwDBLKGkDOZkIiuxwvNO8t-yPMcPM=s64","userId":"13593993050630757383"},"user_tz":-480},"outputId":"47956c3f-f3de-4abd-8bd1-b9f78c604068"},"source":["from google.colab import drive\n","drive.mount('/content/drive', force_remount=True)\n","\n","# import the necessary packages\n","from sklearn.model_selection import train_test_split\n","from google.colab.patches import cv2_imshow\n","import matplotlib.pyplot as plt\n","%matplotlib inline\n","import numpy as np\n","import sklearn \n","import random\n","import pickle\n","import time\n","import cv2\n","import os\n","os.chdir(\"/content/drive/MyDrive/offline/projects/ml/biometricECG/\")\n","!ls"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Mounted at /content/drive\n","apidata.py\t image_siamese.ipynb   mit.ipynb      siamese.py\n","cnn.py\t\t images.py\t       models\t      signal.ipynb\n","data\t\t LICENSE\t       __pycache__    signals.py\n","dummy.py\t logs\t\t       README.md      signal_vgg.ipynb\n","enroll.ipynb\t media\t\t       run.py\t      train.ipynb\n","environment.yml  mit_1d_processing.py  setup.py       trials\n","features.py\t mit_3000.ipynb        siamese.ipynb  venv\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"V2tYUqxxdo-Q"},"source":["# load dataset\n","pickleIn = open('data/ready/pickles/cnn.pickle', 'rb')\n","people, y, x = pickle.load(pickleIn)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":283},"id":"Y2O8VBrwlV9K","executionInfo":{"elapsed":7192,"status":"ok","timestamp":1622743736097,"user":{"displayName":"Axel Mukwena","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjkBUMeSLjKE8qwDBLKGkDOZkIiuxwvNO8t-yPMcPM=s64","userId":"13593993050630757383"},"user_tz":-480},"outputId":"89fa4a41-668d-4347-ee19-29bbfc9e7fc6"},"source":["why = []\n","for i in range(10):\n","  why.append(y[i])\n","  plt.plot(x[i])\n","print(why)\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["['100', '100', '100', '100', '100', '100', '100', '100', '100', '100']\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"u0c6LgQQ7jBn"},"source":["# shuffle data\n","from collections import Counter\n","\n","# print(Counter(y))\n","length = len(y)\n","print(length)\n","data = []\n","for i in range(length):\n","  data.append([y[i], x[i]])\n","\n","print(y)\n","print(y[2])\n","\n","plt.plot(x[2], linewidth=0.8, color=\"k\")\n","plt.show()\n","\n","num = random.randint(0, length)\n","random.seed(num)\n","random.shuffle(data)\n","\n","y, x = [], []\n","for k in range(length):\n","  y.append(data[k][0])\n","  x.append(data[k][1])\n","\n","data, k, length, num = [], 0, 0, 0 # just for memory management\n","\n","print(y)\n","print(y[2])\n","\n","plt.plot(x[2], linewidth=0.8, color=\"k\")\n","plt.show()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"HS_ch_GW7xpq","executionInfo":{"elapsed":3359,"status":"ok","timestamp":1622743755726,"user":{"displayName":"Axel Mukwena","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjkBUMeSLjKE8qwDBLKGkDOZkIiuxwvNO8t-yPMcPM=s64","userId":"13593993050630757383"},"user_tz":-480},"outputId":"55a2cd23-b724-4ab9-ad1b-025ee479ab9c"},"source":["x = np.array(x)\n","\n","print('Before Normalization\\n')\n","print('Shape:', x.shape)\n","print('Min:', x.min(), 'Max:', x.max())\n","print(x.dtype)\n","\n","print('\\nAfter Normalization\\n')\n","\n","x = (x - x.min()) / (x.max() - x.min())\n","y = np.array(y)\n","\n","print('Shape:', x.shape)\n","print('Min:', x.min(), 'Max:', x.max())\n","print(x.dtype)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Before Normalization\n","\n","Shape: (397740, 256)\n","Min: -7.565526962280273 Max: 8.537934070241493\n","float64\n","\n","After Normalization\n","\n","Shape: (397740, 256)\n","Min: 0.0 Max: 1.0\n","float64\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uxQdUnTbYdLk","executionInfo":{"elapsed":1786,"status":"ok","timestamp":1622743762814,"user":{"displayName":"Axel Mukwena","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjkBUMeSLjKE8qwDBLKGkDOZkIiuxwvNO8t-yPMcPM=s64","userId":"13593993050630757383"},"user_tz":-480},"outputId":"7950b2aa-59a0-4242-e53a-cacdb881acdb"},"source":["from sklearn.preprocessing import LabelBinarizer\n","\n","SIG_DIMS = (x.shape[1], 1)\n","\n","# binarize the labels\n","lb = LabelBinarizer()\n","y = lb.fit_transform(y)\n","\n","x_train, x_test, y_train, y_test = train_test_split(\n","    x, y, test_size=0.4, shuffle=True, random_state=42)\n","\n","x_valid, x_test, y_valid, y_test = train_test_split(\n","    x_test, y_test, test_size=0.3, shuffle=True, random_state=42)\n","\n","x, y = [], []  # just for memory management\n","\n","print(x_train.shape)\n","x_train = x_train.reshape(x_train.shape[0], SIG_DIMS[0], SIG_DIMS[1])\n","x_valid = x_valid.reshape(x_valid.shape[0], SIG_DIMS[0], SIG_DIMS[1])\n","x_test = x_test.reshape(x_test.shape[0], SIG_DIMS[0], SIG_DIMS[1])\n","print(x_train.shape)\n","print(x_valid.shape)\n","print(x_test.shape)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["['100' '101' '102' '103' '104' '105' '106' '107' '108' '109' '111' '112'\n"," '113' '114' '115' '116' '117' '118' '119' '121' '122' '123' '124' '1975'\n"," '200' '201' '202' '203' '205' '207' '208' '209' '210' '212' '213' '214'\n"," '215' '217' '219' '220']\n","(238644, 256)\n","(238644, 256, 1)\n","(111367, 256, 1)\n","(47729, 256, 1)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"yJCSqXiMexvO"},"source":["from keras_preprocessing.image import ImageDataGenerator\n","from tensorflow.keras.callbacks import ModelCheckpoint\n","from tensorflow.keras.callbacks import EarlyStopping\n","from tensorflow.keras.callbacks import TensorBoard\n","from tensorflow.keras.models import Sequential\n","from tensorflow.keras.layers import BatchNormalization, concatenate, Conv1D, MaxPool1D, Activation, Flatten, Dropout, Dense, Input, Add\n","from tensorflow.keras.models import Model\n","from tensorflow.keras import backend as K\n","from sklearn.preprocessing import LabelBinarizer\n","from tensorflow.keras.optimizers import Adam\n","from google.colab.patches import cv2_imshow\n","from tensorflow.keras.optimizers import Adam\n","import matplotlib.pyplot as plt\n","from datetime import datetime\n","import tensorflow as tf\n","%load_ext tensorboard\n","from tensorflow.keras import layers\n","%matplotlib inline\n","import numpy as np\n","import random\n","import pickle\n","import time\n","import cv2\n","import os\n","import gc"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"2UqxFXFem26B"},"source":["# initialize the number of epochs to train for, initial learning rate,\n","# batch size, and image dimensions\n","EPOCHS = 100\n","BS = 64\n","SIG_DIMS = (x_train.shape[1], 1)\n","LR = 0.00001\n","decay = LR/EPOCHS\n","adam = Adam(learning_rate=LR,decay=decay)\n","\n","def block(model, fs, ks, ps):\n","  model.add(Conv1D(filters=fs, kernel_size=ks, padding=\"same\"))\n","  model.add(BatchNormalization())\n","  model.add(Activation('relu'))\n","  model.add(MaxPool1D(pool_size=ps, padding='same'))\n","  return model\n","\n","def SPPLayer(inp, spp_windows):\n","  p_poolings = []\n","\n","  for pi in range(len(spp_windows)):\n","    p_poolings.append(Flatten()(MaxPool1D(pool_size=spp_windows[pi], padding='same')(inp)))\n","  out = concatenate(p_poolings, axis=-1)\n","\n","  return out\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"24ywTr35bYeS","executionInfo":{"elapsed":4565446,"status":"ok","timestamp":1622750667652,"user":{"displayName":"Axel Mukwena","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjkBUMeSLjKE8qwDBLKGkDOZkIiuxwvNO8t-yPMcPM=s64","userId":"13593993050630757383"},"user_tz":-480},"outputId":"56045922-9ee4-4af9-9002-903d5e3717da"},"source":["folder = \"models/cnn/\"\n","if not os.path.exists(folder):\n","  os.makedirs(folder)\n","\n","# save the label binarizer to disk\n","f = open(folder + \"lb.pickle\", \"wb\")\n","f.write(pickle.dumps(lb))\n","f.close()\n","\n","# Model\n","model = Sequential()\n","\n","model.add(Conv1D(filters=32, kernel_size=1, padding=\"same\", input_shape=SIG_DIMS))\n","model.add(BatchNormalization())\n","model.add(Activation('relu'))\n","\n","# Blocks\n","model = block(model, 32 * 2, 15, 2)\n","model = block(model, 32 * 4, 15, 2)\n","model = block(model, 32 * 8, 15, 2)\n","model = block(model, 32 * 16, 15, 2)\n","last = 1 + 2 + 4 + 8 + 16\n","model = block(model, 32 * last, 15, 2)\n","\n","# Fully Connected Layer\n","model.add(Flatten())\n","model.add(Dense(32 * last))\n","model.add(BatchNormalization())\n","model.add(Activation('relu'))\n","model.add(Dropout(0.25))\n","\n","# softmax classifier\n","model.add(Dense(len(lb.classes_)))\n","model.add(Activation(\"softmax\"))\n","\n","print(model.summary())\n","\n","model.compile(optimizer=adam, loss=\"categorical_crossentropy\", metrics=[\"accuracy\"])\n","\n","STEPS_PER_EPOCH = len(x_train) // BS\n","VAL_STEPS_PER_EPOCH = len(x_valid) // BS\n","\n","bestmodel = folder + \"debbis\"\n","checkpointer = ModelCheckpoint(filepath=bestmodel, verbose=1, save_best_only=True)\n","early_stopping = EarlyStopping(monitor='val_loss', patience=5)\n","\n","# Define the Keras TensorBoard callback.\n","logdir = folder + \"logs/fit/\" + datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n","tensorboard_callback = TensorBoard(log_dir=logdir)\n","\n","# fit network\n","t = time.time()\n","\n","H = model.fit(x_train, y_train, batch_size=BS,\n","              validation_data=(x_valid, y_valid),\n","              steps_per_epoch=STEPS_PER_EPOCH,\n","              validation_steps=VAL_STEPS_PER_EPOCH,\n","              epochs=EPOCHS, verbose=1, \n","              callbacks=[tensorboard_callback, checkpointer, early_stopping])\n","\n","print('\\nTraining time: ', time.time() - t)\n","\n","# save the model to disk\n","model.save(bestmodel)\n","\n","# evaluate model\n","_, accuracy = model.evaluate(x_test, y_test, batch_size=BS, verbose=1)\n","print('\\n', 'Test accuracy:', accuracy, '\\n')\n","\n","# plot the training loss and accuracy\n","acc = H.history['accuracy']\n","val_acc = H.history['val_accuracy']\n","\n","loss = H.history['loss']\n","val_loss = H.history['val_loss']\n","\n","plt.figure(figsize=(8, 8))\n","plt.subplot(2, 1, 1)\n","plt.plot(acc, label='Training Accuracy')\n","plt.plot(val_acc, label='Validation Accuracy')\n","plt.legend(loc='lower right')\n","plt.ylabel('Accuracy')\n","plt.ylim([min(plt.ylim()),1])\n","plt.title('Training and Validation Accuracy')\n","\n","plt.subplot(2, 1, 2)\n","plt.plot(loss, label='Training Loss')\n","plt.plot(val_loss, label='Validation Loss')\n","plt.legend(loc='upper right')\n","plt.ylabel('Cross Entropy')\n","plt.ylim([0,1.0])\n","plt.title('Training and Validation Loss')\n","plt.xlabel('epoch')\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Model: \"sequential\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","conv1d (Conv1D)              (None, 256, 128)          256       \n","_________________________________________________________________\n","batch_normalization (BatchNo (None, 256, 128)          512       \n","_________________________________________________________________\n","activation (Activation)      (None, 256, 128)          0         \n","_________________________________________________________________\n","conv1d_1 (Conv1D)            (None, 256, 256)          491776    \n","_________________________________________________________________\n","batch_normalization_1 (Batch (None, 256, 256)          1024      \n","_________________________________________________________________\n","activation_1 (Activation)    (None, 256, 256)          0         \n","_________________________________________________________________\n","max_pooling1d (MaxPooling1D) (None, 128, 256)          0         \n","_________________________________________________________________\n","conv1d_2 (Conv1D)            (None, 128, 512)          1966592   \n","_________________________________________________________________\n","batch_normalization_2 (Batch (None, 128, 512)          2048      \n","_________________________________________________________________\n","activation_2 (Activation)    (None, 128, 512)          0         \n","_________________________________________________________________\n","max_pooling1d_1 (MaxPooling1 (None, 64, 512)           0         \n","_________________________________________________________________\n","conv1d_3 (Conv1D)            (None, 64, 1024)          7865344   \n","_________________________________________________________________\n","batch_normalization_3 (Batch (None, 64, 1024)          4096      \n","_________________________________________________________________\n","activation_3 (Activation)    (None, 64, 1024)          0         \n","_________________________________________________________________\n","max_pooling1d_2 (MaxPooling1 (None, 32, 1024)          0         \n","_________________________________________________________________\n","conv1d_4 (Conv1D)            (None, 32, 1792)          27526912  \n","_________________________________________________________________\n","batch_normalization_4 (Batch (None, 32, 1792)          7168      \n","_________________________________________________________________\n","activation_4 (Activation)    (None, 32, 1792)          0         \n","_________________________________________________________________\n","max_pooling1d_3 (MaxPooling1 (None, 16, 1792)          0         \n","_________________________________________________________________\n","flatten (Flatten)            (None, 28672)             0         \n","_________________________________________________________________\n","dense (Dense)                (None, 1792)              51382016  \n","_________________________________________________________________\n","batch_normalization_5 (Batch (None, 1792)              7168      \n","_________________________________________________________________\n","activation_5 (Activation)    (None, 1792)              0         \n","_________________________________________________________________\n","dropout (Dropout)            (None, 1792)              0         \n","_________________________________________________________________\n","dense_1 (Dense)              (None, 40)                71720     \n","_________________________________________________________________\n","activation_6 (Activation)    (None, 40)                0         \n","=================================================================\n","Total params: 89,326,632\n","Trainable params: 89,315,624\n","Non-trainable params: 11,008\n","_________________________________________________________________\n","None\n","Epoch 1/100\n","3728/3728 [==============================] - 411s 108ms/step - loss: 0.0754 - accuracy: 0.9808 - val_loss: 0.0300 - val_accuracy: 0.9924\n","\n","Epoch 00001: val_loss improved from inf to 0.03004, saving model to models/auth/8/debbis\n","INFO:tensorflow:Assets written to: models/auth/8/debbis/assets\n","Epoch 2/100\n","3728/3728 [==============================] - 400s 107ms/step - loss: 0.0218 - accuracy: 0.9937 - val_loss: 0.0174 - val_accuracy: 0.9951\n","\n","Epoch 00002: val_loss improved from 0.03004 to 0.01742, saving model to models/auth/8/debbis\n","INFO:tensorflow:Assets written to: models/auth/8/debbis/assets\n","Epoch 3/100\n","3728/3728 [==============================] - 399s 107ms/step - loss: 0.0124 - accuracy: 0.9963 - val_loss: 0.0151 - val_accuracy: 0.9957\n","\n","Epoch 00003: val_loss improved from 0.01742 to 0.01513, saving model to models/auth/8/debbis\n","INFO:tensorflow:Assets written to: models/auth/8/debbis/assets\n","Epoch 4/100\n","3728/3728 [==============================] - 399s 107ms/step - loss: 0.0077 - accuracy: 0.9978 - val_loss: 0.0075 - val_accuracy: 0.9978\n","\n","Epoch 00004: val_loss improved from 0.01513 to 0.00753, saving model to models/auth/8/debbis\n","INFO:tensorflow:Assets written to: models/auth/8/debbis/assets\n","Epoch 5/100\n","3728/3728 [==============================] - 400s 107ms/step - loss: 0.0056 - accuracy: 0.9984 - val_loss: 0.0120 - val_accuracy: 0.9963\n","\n","Epoch 00005: val_loss did not improve from 0.00753\n","Epoch 6/100\n","3728/3728 [==============================] - 400s 107ms/step - loss: 0.0039 - accuracy: 0.9990 - val_loss: 0.0052 - val_accuracy: 0.9988\n","\n","Epoch 00006: val_loss improved from 0.00753 to 0.00524, saving model to models/auth/8/debbis\n","INFO:tensorflow:Assets written to: models/auth/8/debbis/assets\n","Epoch 7/100\n","3728/3728 [==============================] - 399s 107ms/step - loss: 0.0034 - accuracy: 0.9990 - val_loss: 0.0071 - val_accuracy: 0.9982\n","\n","Epoch 00007: val_loss did not improve from 0.00524\n","Epoch 8/100\n","3728/3728 [==============================] - 398s 107ms/step - loss: 0.0028 - accuracy: 0.9992 - val_loss: 0.0103 - val_accuracy: 0.9968\n","\n","Epoch 00008: val_loss did not improve from 0.00524\n","Epoch 9/100\n","3728/3728 [==============================] - 397s 107ms/step - loss: 0.0025 - accuracy: 0.9993 - val_loss: 0.0143 - val_accuracy: 0.9962\n","\n","Epoch 00009: val_loss did not improve from 0.00524\n","Epoch 10/100\n","3728/3728 [==============================] - 397s 106ms/step - loss: 0.0020 - accuracy: 0.9995 - val_loss: 0.0038 - val_accuracy: 0.9990\n","\n","Epoch 00010: val_loss improved from 0.00524 to 0.00376, saving model to models/auth/8/debbis\n","INFO:tensorflow:Assets written to: models/auth/8/debbis/assets\n","Epoch 11/100\n","3728/3728 [==============================] - 397s 107ms/step - loss: 0.0018 - accuracy: 0.9995 - val_loss: 0.0040 - val_accuracy: 0.9992\n","\n","Epoch 00011: val_loss did not improve from 0.00376\n","Epoch 12/100\n","3728/3728 [==============================] - 397s 107ms/step - loss: 0.0017 - accuracy: 0.9995 - val_loss: 0.0028 - val_accuracy: 0.9994\n","\n","Epoch 00012: val_loss improved from 0.00376 to 0.00279, saving model to models/auth/8/debbis\n","INFO:tensorflow:Assets written to: models/auth/8/debbis/assets\n","Epoch 13/100\n","3728/3728 [==============================] - 398s 107ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 0.0035 - val_accuracy: 0.9992\n","\n","Epoch 00013: val_loss did not improve from 0.00279\n","Epoch 14/100\n","3728/3728 [==============================] - 397s 107ms/step - loss: 0.0013 - accuracy: 0.9997 - val_loss: 0.0049 - val_accuracy: 0.9988\n","\n","Epoch 00014: val_loss did not improve from 0.00279\n","Epoch 15/100\n","3728/3728 [==============================] - 397s 107ms/step - loss: 0.0013 - accuracy: 0.9997 - val_loss: 0.0039 - val_accuracy: 0.9992\n","\n","Epoch 00015: val_loss did not improve from 0.00279\n","Epoch 16/100\n","3728/3728 [==============================] - 398s 107ms/step - loss: 0.0013 - accuracy: 0.9997 - val_loss: 0.0034 - val_accuracy: 0.9991\n","\n","Epoch 00016: val_loss did not improve from 0.00279\n","Epoch 17/100\n","3728/3728 [==============================] - 397s 107ms/step - loss: 9.6815e-04 - accuracy: 0.9998 - val_loss: 0.0031 - val_accuracy: 0.9994\n","\n","Epoch 00017: val_loss did not improve from 0.00279\n","\n","Training time:  6839.25846529007\n","INFO:tensorflow:Assets written to: models/auth/8/debbis/assets\n","746/746 [==============================] - 16s 21ms/step - loss: 0.0023 - accuracy: 0.9994\n","\n"," Test accuracy: 0.9993923902511597 \n","\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x576 with 2 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"w8TG61tXHqib"},"source":["%tensorboard --logdir models/auth/8/logs/fit"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uTPBN9lG7FZj","executionInfo":{"elapsed":4306,"status":"ok","timestamp":1622738285661,"user":{"displayName":"Axel Mukwena","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjkBUMeSLjKE8qwDBLKGkDOZkIiuxwvNO8t-yPMcPM=s64","userId":"13593993050630757383"},"user_tz":-480},"outputId":"82e481c4-c0f6-4a2f-fcf2-152323a1dd99"},"source":["_, accuracy = model.evaluate(x_test, y_test)\n","print('\\n', 'Test accuracy:', accuracy, '\\n')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["375/375 [==============================] - 4s 9ms/step - loss: 0.0058 - accuracy: 0.9987\n","\n"," Test accuracy: 0.9986666440963745 \n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ycqdMq_aqMNJ","executionInfo":{"elapsed":3015,"status":"ok","timestamp":1622739147553,"user":{"displayName":"Axel Mukwena","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjkBUMeSLjKE8qwDBLKGkDOZkIiuxwvNO8t-yPMcPM=s64","userId":"13593993050630757383"},"user_tz":-480},"outputId":"4c7bab7b-1494-4bea-eb1b-77e8c103485e"},"source":["from sklearn.metrics import classification_report\n","\n","lbb = LabelBinarizer()\n","predictions = model.predict(x_test, batch_size=BS, verbose=1)\n","y_pred_bool = np.argmax(predictions, axis=1)\n","y_pred_bool = lbb.fit_transform(y_pred_bool)\n","print(classification_report(y_test, y_pred_bool))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["188/188 [==============================] - 2s 11ms/step\n","              precision    recall  f1-score   support\n","\n","           0       1.00      1.00      1.00       303\n","           1       1.00      1.00      1.00       314\n","           2       1.00      1.00      1.00       299\n","           3       1.00      1.00      1.00       328\n","           4       1.00      1.00      1.00       305\n","           5       1.00      1.00      1.00       313\n","           6       1.00      1.00      1.00       318\n","           7       1.00      1.00      1.00       324\n","           8       1.00      1.00      1.00       290\n","           9       1.00      1.00      1.00       303\n","          10       1.00      1.00      1.00       319\n","          11       1.00      1.00      1.00       301\n","          12       1.00      1.00      1.00       297\n","          13       1.00      1.00      1.00       317\n","          14       1.00      1.00      1.00       289\n","          15       0.99      1.00      1.00       281\n","          16       1.00      1.00      1.00       330\n","          17       1.00      1.00      1.00       319\n","          18       1.00      1.00      1.00       284\n","          19       1.00      1.00      1.00       295\n","          20       1.00      1.00      1.00       279\n","          21       1.00      1.00      1.00       279\n","          22       1.00      1.00      1.00       323\n","          23       1.00      1.00      1.00       292\n","          24       1.00      1.00      1.00       309\n","          25       1.00      0.99      1.00       311\n","          26       1.00      1.00      1.00       288\n","          27       1.00      1.00      1.00       268\n","          28       1.00      1.00      1.00       307\n","          29       1.00      1.00      1.00       292\n","          30       1.00      1.00      1.00       261\n","          31       1.00      1.00      1.00       313\n","          32       1.00      1.00      1.00       299\n","          33       1.00      1.00      1.00       269\n","          34       1.00      1.00      1.00       304\n","          35       1.00      1.00      1.00       282\n","          36       1.00      1.00      1.00       266\n","          37       1.00      1.00      1.00       298\n","          38       1.00      1.00      1.00       338\n","          39       1.00      1.00      1.00       293\n","\n","   micro avg       1.00      1.00      1.00     12000\n","   macro avg       1.00      1.00      1.00     12000\n","weighted avg       1.00      1.00      1.00     12000\n"," samples avg       1.00      1.00      1.00     12000\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"5AxIuyJBjRmn"},"source":["predictions = model.predict(x_test)\n","print(len(predictions))\n","\n","up, down = [], []\n","for i in predictions:\n","  pred = max(i)\n","  if pred >= 0.99:\n","    up.append(pred)\n","  else:\n","    down.append(pred)\n","  \n","print(\"Up:\", len(up))\n","print(up, \"\\n\")\n","print(\"Down:\", len(down))\n","print(down, \"\\n\")\n","\n","fig = plt.figure(figsize=(64, 54))\n","for i, idx in enumerate(np.random.choice(x_test.shape[0], size=225, replace=False)):\n","    pred_idx = np.argmax(predictions[idx])\n","    true_idx = np.argmax(y_test[idx])\n","    probs = predictions[pred_idx]\n","    prob = max(predictions[pred_idx])\n","    ax = fig.add_subplot(15, 15, i + 1, xticks=[], yticks=[])\n","    ax.plot(x_test[idx])\n","    ax.set_title(\"T: {} P: {} {:.6f}\".format(lb.classes_[true_idx], lb.classes_[pred_idx], prob),\n","                 color=(\"green\" if pred_idx == true_idx else \"red\"))\n","    # ax.set_xlabel(probs)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"g3w0oOnco_lE","executionInfo":{"elapsed":6145,"status":"ok","timestamp":1622751428945,"user":{"displayName":"Axel Mukwena","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjkBUMeSLjKE8qwDBLKGkDOZkIiuxwvNO8t-yPMcPM=s64","userId":"13593993050630757383"},"user_tz":-480},"outputId":"75652465-2ead-451f-8572-d7f0a78d9312"},"source":["def plot_confusion_matrix(cm, classes,\n","                        normalize=False,\n","                        title='Confusion matrix',\n","                        cmap=plt.cm.Blues):\n","    \"\"\"\n","    This function prints and plots the confusion matrix.\n","    Normalization can be applied by setting `normalize=True`.\n","    \"\"\"\n","    plt.figure(figsize=(20, 20))\n","    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n","    plt.title(title)\n","    plt.colorbar()\n","    tick_marks = np.arange(len(classes))\n","    plt.xticks(tick_marks, classes, rotation=45)\n","    plt.yticks(tick_marks, classes)\n","\n","    if normalize:\n","        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n","        print(\"Normalized confusion matrix\")\n","    else:\n","        print('Confusion matrix, without normalization')\n","\n","    print(cm)\n","\n","    thresh = cm.max() / 2.\n","    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n","        plt.text(j, i, cm[i, j],\n","            horizontalalignment=\"center\",\n","            color=\"white\" if cm[i, j] > thresh else \"black\")\n","\n","    plt.tight_layout()\n","    plt.ylabel('True label')\n","    plt.xlabel('Predicted label')\n","\n","\n","%matplotlib inline\n","from sklearn.metrics import confusion_matrix\n","import itertools\n","import matplotlib.pyplot as plt\n","\n","test = np.argmax(y_test, axis=1)\n","preds = np.argmax(predictions, axis=1)\n","cm = confusion_matrix(y_true=test, y_pred=preds)\n","plot_confusion_matrix(cm=cm, classes=lb.classes_, title='Confusion Matrix')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Confusion matrix, without normalization\n","[[1172    0    0 ...    0    0    0]\n"," [   0 1169    0 ...    0    0    0]\n"," [   0    0 1261 ...    0    0    0]\n"," ...\n"," [   0    0    0 ... 1279    0    0]\n"," [   0    0    0 ...    0 1168    0]\n"," [   0    0    0 ...    0    0 1158]]\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1440x1440 with 2 Axes>"]},"metadata":{"tags":[]}}]}]}